The use of digital image correlation for measurement of strain fields in a novel wireless intraspinal microstimulation interface
Bibliographic record
Abstract
BACKGROUND: Intraspinal microstimulation (ISMS) has emerged as a promising neuromodulation technique for restoring standing and overground walking in individuals with spinal cord injury. Current and emerging ISMS implant designs connect the electrodes to the stimulator through lead wires that cross the dura mater. To reduce possible complications associated with transdural leads such as tethering and leakage of cerebrospinal fluid, we aim to develop a wireless, fully intradural ISMS implant based on our prior work in the cortex with the Wireless Floating Microelectrode Array (WFMA). Although we have extensive data about WFMA cortical stability, its mechanical and electrical stability in the spinal cord remains unknown. One of the quantifiable metrics to assess long-term implant stability is mechanical strain. OBJECTIVE: The aim of the current work is to develop a method to assess implant stability by measuring strain fields in a surrogate of the human spinal cord. METHODS: A physical model of the spinal cord was studied using an electromechanical testing apparatus, simulating typical spinal cord motion. Strain fields were digitally analyzed using an optical method known as digital image correlation (DIC). RESULTS: Displacement and strain were visualized using contour plots. The strain values in the vicinity of each WFMA device were significantly different from the strain values in the same locations in the control surrogate spinal cord. CONCLUSION: The results demonstrate that DIC can be used for in-vitro screening of intraspinal implants. Accurate optical strain measurements will enable researchers to optimize implant design over a wide range of motion conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".